Lyceum: An efficient and scalable ecosystem for robot learning

Colin X. Summers, Kendall Lowrey, Aravind Rajeswaran, Emanuel Todorov, Siddhartha Srinivasa

TL;DR: A high performance robotics simulation and algorithm development framework.

Abstract: We introduce Lyceum, a high-performance computational ecosystem for robotlearning. Lyceum is built on top of the Julia programming language and theMuJoCo physics simulator, combining the ease-of-use of a high-level program-ming language with the performance of native C. Lyceum is up to 10-20Xfaster compared to other popular abstractions like OpenAI’sGymand Deep-Mind’sdm-control. This substantially reduces training time for various re-inforcement learning algorithms; and is also fast enough to support real-timemodel predictive control with physics simulators. Lyceum has a straightfor-ward API and supports parallel computation across multiple cores or machines.The code base, tutorials, and demonstration videos can be found at: https://sites.google.com/view/lyceum-anon.

Code: https://sites.google.com/view/lyceum-anon.

Keywords: Robotics, RL, Julia

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/lyceum-an-efficient-and-scalable-ecosystem/code)